Introduction to structured population models and demographic distribution models (IIPM01R)
5th May 2025
£400.00
Event Date
Monday, November 18th, 2019
Course Format
Pre Recorded
About This Course
Structured population models have become a central tool to explore evolutionary and ecological processes, such as selection gradients or responses to climate change, based on field demographic data. With recent advances in theoretical ecology and computing power, these models have further evolved. Over the last decade, Integral Projection Models (IPMs), a generalization of Matrix Populations Models, have gained popularity because of their simplicity, robustness, and flexibility. This course will draw on the connections between these approaches to help researchers understand and predict how individual performance scales to population-level dynamics. The course will cover all aspects of analysis, including data preparation, regressions, matrix/intregral projection modeling, and sythesis for understanding population structure. Special enphasis with be places on demogrpahically-driven distribution models. Participants are encouraged to bring their own data to analyze, however example data sets will be provided for model exploration. Each day, we’ll reserve time for open work sessions where students can receive mentoring while applying new skills to their own data sets or example data sets provided by the instructors.
Intended Audiences
Course Details
Last Up-Dated – 22;11;2019
Duration – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
Basic familiarity with regression is essential. Experience with linear algebra and Bayesian methods is helpful, but introductions will be provided.
Assumed computer background
Basic experience with R, including regressions, graphics, and manipulating data frames. This ‘experience’ often corresponds to one or more years of using R regularly.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Day 1 – Approx. 7 hours
Day 1 begins with a background on the similarities and differences between the two primary types of structured populations models and methods for calculating common population statistics.
1) A background in Matrix Population Models
2) Extensions to Integral Population Models, and vital rate regressions
3) Population statistics – lambda, elasticity, sensitivity, and more
Day 2 – Approx. 7 hours
Day 2, we’ll look at some of the challenges of modeling vital rates for different life histories and some common pitfalls. We’ll explore practical challenges with some detailed examples.
4) Individual growth, stasis, and shrinkage
5) Survival
6) Fecundity is complicated
7) Example: an endangered overstory shrub
8) Example: an invasive biennial herb
Day 3 – Approx. 7 hours
Day 3 will focus on checking and validating IPMs from both a statistical and biological perspective. We’ll extend IPMs coupled with environmental data to build dynamic species distribution models.
9) Model diagnostics
10) Improving IPMs – how do vital rate regressions influence population statistics?
11) Demographic distribution models
Day 4 – Approx. 7 hours
Day 4 will focus on producing measures of timing, individuals classified by multiple variables, and special challenges for organisms that live much longer than the study period.
12) Age from stage statistics
13) Individuals characterised by multiple states (e.g., age and stage)
14) Tree Demography
Day 5 – Approx. 7 hours
Day 5 will conclude with some mode advanced statistical methods to incorporate uncertainty in predictions and infer or working around data missing from a species life history.
15) Bayesian methods – capturing uncertainty
16) Inferring missing life history information
Course Instructor
Dr. Antoine Becker-Scarpitta
Works at – University of Helsink
Teaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.
